# Mindanao State University
# General Santos City
# Introduction to R base commands
# Submitted by: Joshua Marie H. Casador
# Prepared by: Prof. Carlito O. Daarol
# Faculty
# Math Department
# March 16, 2023
# Lab Exercise 1: How to create Lines in with different styles in R
# Step 1: Create Data
x <- 1:10 # Create example data
y <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9) # using the c() function to create an
array
## function (data = NA, dim = length(data), dimnames = NULL)
## {
## if (is.atomic(data) && !is.object(data))
## return(.Internal(array(data, dim, dimnames)))
## data <- as.vector(data)
## if (is.object(data)) {
## dim <- as.integer(dim)
## if (!length(dim))
## stop("'dim' cannot be of length 0")
## vl <- prod(dim)
## if (length(data) != vl) {
## if (vl > .Machine$integer.max)
## stop("'dim' specifies too large an array")
## data <- rep_len(data, vl)
## }
## if (length(dim))
## dim(data) <- dim
## if (is.list(dimnames) && length(dimnames))
## dimnames(data) <- dimnames
## data
## }
## else .Internal(array(data, dim, dimnames))
## }
## <bytecode: 0x000001727e6200b8>
## <environment: namespace:base>
# Step 2: Plot the line graph using the base plot() command
plot(x, y, type = "l")
# Step 3: Add Main Title & Change Axis Labels
plot(x, y, type = "l",
main = "Hello: This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values")
# Step 4: Add color to the line using the col command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
col = "red")
# Step 5: Modify Thickness of Line using lwd command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=7,
col = "green")
# Step 6: Add points to line graph by changing the type command
plot(x, y, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# Lab Exercise 2: How to create Lines in with different styles in R
# Step1: Assign values for different lines. We enclose the entire
# line with parenthesis symbol to force R to display the results instantly
# set the same value for the x variable
(x <- 1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
# set different values for y variables
(y1 <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9))
## [1] 3 1 5 2 3 8 4 7 6 9
(y2 <- c(5, 1, 4, 6, 2, 3, 7, 8, 2, 8))
## [1] 5 1 4 6 2 3 7 8 2 8
(y3 <- c(3, 3, 3, 3, 4, 4, 5, 5, 7, 7))
## [1] 3 3 3 3 4 4 5 5 7 7
# Plot first the pair x and y1.
plot(x, y1, type = "b",pch = 16,
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# Lab Exercise 2: How to create Lines in with different styles in R
# Step1: Assign values for different lines. We enclose the entire
# line with parenthesis symbol to force R to display the results instantly
# set the same value for the x variable
(x <- 1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
# set different values for y variables
(y1 <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9))
## [1] 3 1 5 2 3 8 4 7 6 9
(y2 <- c(5, 1, 4, 6, 2, 3, 7, 8, 2, 8))
## [1] 5 1 4 6 2 3 7 8 2 8
(y3 <- c(3, 3, 3, 3, 4, 4, 5, 5, 7, 7))
## [1] 3 3 3 3 4 4 5 5 7 7
# Plot first the pair x and y1.
plot(x, y1, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# then Add the two lines (for x,y2) and (x,y3)
lines(x, y2, type = "b", col = "red",lwd=3)
lines(x, y3, type = "b", col = "green",lwd=3)
# Add legend to the plot
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Step 2: Create Different Point Symbol for Each
# Line using the pch command
plot(x, y1, type = "b",pch = 16,
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
lines(x, y2, type = "b", col = "red",lwd=3, pch = 15)
lines(x, y3, type = "b", col = "green",lwd=3, pch = 8)
# Add legend
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Lab Exercise 3: Create Line graph without x values
Pupils <- c(3.55 ,3.54 ,3.53 ,3.61 ,3.65 ,3.63 ,3.61
,3.61 ,3.59 ,3.63 ,3.59 ,3.63 ,3.62 ,3.62
,3.59 ,3.63 ,3.62 ,3.65 ,3.65)
# get number of elements of Pupils
length(Pupils)
## [1] 19
# Display the elements of Pupils
Pupils
## [1] 3.55 3.54 3.53 3.61 3.65 3.63 3.61 3.61 3.59 3.63 3.59 3.63 3.62 3.62 3.59
## [16] 3.63 3.62 3.65 3.65
# You can obtain the plot without x values
plot(Pupils, type = 'o')
# Lab Exercise 4: How to Create vertical, horizontal lines
# We will use buit-in cars dataset in R
# display the cars dataset
cars
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
## 7 10 18
## 8 10 26
## 9 10 34
## 10 11 17
## 11 11 28
## 12 12 14
## 13 12 20
## 14 12 24
## 15 12 28
## 16 13 26
## 17 13 34
## 18 13 34
## 19 13 46
## 20 14 26
## 21 14 36
## 22 14 60
## 23 14 80
## 24 15 20
## 25 15 26
## 26 15 54
## 27 16 32
## 28 16 40
## 29 17 32
## 30 17 40
## 31 17 50
## 32 18 42
## 33 18 56
## 34 18 76
## 35 18 84
## 36 19 36
## 37 19 46
## 38 19 68
## 39 20 32
## 40 20 48
## 41 20 52
## 42 20 56
## 43 20 64
## 44 22 66
## 45 23 54
## 46 24 70
## 47 24 92
## 48 24 93
## 49 24 120
## 50 25 85
# get the number of rows and columns using dim() command
dim(cars)
## [1] 50 2
# display the variable names of the cars dataset
names(cars)
## [1] "speed" "dist"
# display only the first column of the dataset
cars$speed # using the column name
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
cars[,1] # using the column number
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
# Remarks: the following commands will give you the same result
plot(cars,) # using the comma after the name
plot(cars[,1],cars[,2]) # using the column index 1 and 2
attach(cars); plot(speed,dist) # using the attach command to load the variables
plot(cars$speed,cars$dist) # using the dollar notation
# combine all 4 plots using the par() command
par(mfrow = c(2,2)) # set a 2x2 plot output
plot(cars,) # using the comma after the name
plot(cars[,1],cars[,2]) # using the column index 1 and 2
attach(cars); plot(speed,dist) # using the attach command to load the variables
## The following objects are masked from cars (pos = 3):
##
## dist, speed
plot(cars$speed,cars$dist) # using the dollar notation
par(mfrow = c(1,1)) # reset to default plot setting
# Problem: Create vertical lines using the v command
plot(cars)
abline(v = 15, col = "darkgreen",lwd=3) # vertical line
abline(v = 10, col = "blue",lwd=3) # vertical line
# Problem: Create horizontal lines using the h command
abline(h = 80, col = "darkgreen",lwd=3) # vertical line
abline(h = 20, col = "blue",lwd=3) # vertical line
# Create more lines simultaneously, using a vector of values
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2), # line thickness
lty = c(2,2,2)) # dashed lines
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2)) # line thickness and solid lines
# create horizontal lines
plot(cars)
abline(h = 60, col = "red",lty = 1, lwd = 3)
abline(h = 100, col = "red",lty = 2, lwd = 3)
abline(h = 20, col = "red",lty = 3, lwd = 3)
# Lab Exercise 5: How to Plot data by group
# We will use buit-in iris dataset in R
# this dataset is a collection of 4 species of flowers with different
# sepal length and width and also with different petal length and width
dim(iris) # iris dataset has 150 rows and 5 columns
## [1] 150 5
names(iris)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
# two different commands to get the frequency table
table(iris$Species) # refer to the dataset by variable name
##
## setosa versicolor virginica
## 50 50 50
table(iris[,5]) # refer to the dataset by column number
##
## setosa versicolor virginica
## 50 50 50
# get summary of all columns
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
#create scatterplot of sepal width vs. sepal length
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=19)
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=1)
# another way to retrieve columns of data
PL <- iris$Petal.Length
PW <- iris$Petal.Width
plot(PL, PW)
# add color by species
plot(PL, PW, col = iris$Species, main= "My Plot")
# draw a line along with the distribution of points
# using the abline and lm commands
abline(lm(PW ~ PL))
# add text annotation
text(5, 0.5, "Regression Line")
legend("topleft", # specify the location of the legend
levels(iris$Species), # specify the levels of species
pch = 1:3, # specify three symbols used for the three species
col = 1:3 # specify three colors for the three species
)
# Lab Exercise 6: Generate advance scatter plot
pairs(iris, col = rainbow(3)[iris$Species]) # set colors by species
# How to filter data for each flower
(Versicolor <- subset(iris, Species == "versicolor"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
(Setosa <- subset(iris, Species == "setosa"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
(Virginica <- subset(iris, Species == "virginica"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
# Draw boxplot for each type of flower
boxplot(Versicolor[,1:4], main="Versicolor, Rainbow Palette",ylim =
c(0,8),las=2, col=rainbow(4))
boxplot(Setosa[,1:4], main="Setosa, Heat color Palette",ylim = c(0,8),las=2,
col=heat.colors(4))
boxplot(Virginica[,1:4], main="Virginica, Topo colors Palette",ylim =
c(0,8),las=2, col=topo.colors(4))
# How to filter data for each flower
(Versicolor <- subset(iris, Species == "versicolor"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
(Setosa <- subset(iris, Species == "setosa"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
(Virginica <- subset(iris, Species == "virginica"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
# Draw boxplot for each type of flower
boxplot(Versicolor[,1:4], main="Versicolor, Rainbow Palette",ylim =
c(0,8),las=2, col=rainbow(4))
boxplot(Setosa[,1:4], main="Setosa, Heat color Palette",ylim = c(0,8),las=2,
col=heat.colors(4))
boxplot(Virginica[,1:4], main="Virginica, Topo colors Palette",ylim =
c(0,8),las=2, col=topo.colors(4))
# Lab Exercise 7: How to load external datasets
# From a local directory
# the folder that contains the file should be specified completely
# using the forward slash symbol instead of the backward splash
library(readr)
cancer <- read_csv("files/Cancer.csv")
## Rows: 173 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): country, continent
## dbl (15): incomeperperson, alcconsumption, armedforcesrate, breastcancer, co...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
dim(cancer)
## [1] 173 17
names(cancer)
## [1] "country" "incomeperperson" "alcconsumption"
## [4] "armedforcesrate" "breastcancer" "co2emissions"
## [7] "femaleemployrate" "hivrate" "internetuserate"
## [10] "lifeexpectancy" "oilperperson" "polityscore"
## [13] "relectricperperson" "suicideper100th" "employrate"
## [16] "urbanrate" "continent"
# compute mean value for every continent
(means <- round(tapply(cancer$breastcancer, cancer$continent, mean),
digits=2))
## AF AS EE LATAM NORAM OC WE
## 24.02 24.51 49.44 36.70 71.73 45.80 74.80
# draw boxplot per continent
boxplot(cancer$breastcancer ~ cancer$continent, main= "Breast cancer by
continent (brown dot = mean value)", xlab="continents", ylab="new
cases per 100,00 residents", col=rainbow(7))
# insert the mean value using brown dot
points(means, col="brown", pch=18)
# Lab Exercise 8: How to load external datasets and change data layout
library(readr)
hsb2 <- read_csv("files/hsb2.csv")
## New names:
## Rows: 200 Columns: 12
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," dbl
## (12): ...1, id, female, race, ses, schtyp, prog, read, write, math, scie...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
# display only the top 6 rows
head(hsb2)
## # A tibble: 6 × 12
## ...1 id female race ses schtyp prog read write math science socst
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 70 0 4 1 1 1 57 52 41 47 57
## 2 2 121 1 4 2 1 3 68 59 53 63 61
## 3 3 86 0 4 3 1 1 44 33 54 58 31
## 4 4 141 0 4 3 1 3 63 44 47 53 56
## 5 5 172 0 4 2 1 2 47 52 57 53 61
## 6 6 113 0 4 2 1 2 44 52 51 63 61
# display only the last 6 rows
tail(hsb2)
## # A tibble: 6 × 12
## ...1 id female race ses schtyp prog read write math science socst
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 195 179 1 4 2 2 2 47 65 60 50 56
## 2 196 31 1 2 2 2 1 55 59 52 42 56
## 3 197 145 1 4 2 1 3 42 46 38 36 46
## 4 198 187 1 4 2 2 1 57 41 57 55 52
## 5 199 118 1 4 2 1 1 55 62 58 58 61
## 6 200 137 1 4 3 1 2 63 65 65 53 61
# delete redundant first column (run only once)
(hsb2<- hsb2 [-1])
## # A tibble: 200 × 11
## id female race ses schtyp prog read write math science socst
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 70 0 4 1 1 1 57 52 41 47 57
## 2 121 1 4 2 1 3 68 59 53 63 61
## 3 86 0 4 3 1 1 44 33 54 58 31
## 4 141 0 4 3 1 3 63 44 47 53 56
## 5 172 0 4 2 1 2 47 52 57 53 61
## 6 113 0 4 2 1 2 44 52 51 63 61
## 7 50 0 3 2 1 1 50 59 42 53 61
## 8 11 0 1 2 1 2 34 46 45 39 36
## 9 84 0 4 2 1 1 63 57 54 58 51
## 10 48 0 3 2 1 2 57 55 52 50 51
## # ℹ 190 more rows
# Remarks
# hsb2 dataset consists of 200 selected random samples from senior
# high school students in the US.
# We want to compare the student performance across different subjects
# change data layout by grouping different subjects
# into one column using melt() command. Install first reshape2 package
# install.packages("reshape2")
library(reshape2)
(hsb2_long <- melt(hsb2, measure.vars =
c("read","write","math","science","socst")))
## id female race ses schtyp prog variable value
## 1 70 0 4 1 1 1 read 57
## 2 121 1 4 2 1 3 read 68
## 3 86 0 4 3 1 1 read 44
## 4 141 0 4 3 1 3 read 63
## 5 172 0 4 2 1 2 read 47
## 6 113 0 4 2 1 2 read 44
## 7 50 0 3 2 1 1 read 50
## 8 11 0 1 2 1 2 read 34
## 9 84 0 4 2 1 1 read 63
## 10 48 0 3 2 1 2 read 57
## 11 75 0 4 2 1 3 read 60
## 12 60 0 4 2 1 2 read 57
## 13 95 0 4 3 1 2 read 73
## 14 104 0 4 3 1 2 read 54
## 15 38 0 3 1 1 2 read 45
## 16 115 0 4 1 1 1 read 42
## 17 76 0 4 3 1 2 read 47
## 18 195 0 4 2 2 1 read 57
## 19 114 0 4 3 1 2 read 68
## 20 85 0 4 2 1 1 read 55
## 21 167 0 4 2 1 1 read 63
## 22 143 0 4 2 1 3 read 63
## 23 41 0 3 2 1 2 read 50
## 24 20 0 1 3 1 2 read 60
## 25 12 0 1 2 1 3 read 37
## 26 53 0 3 2 1 3 read 34
## 27 154 0 4 3 1 2 read 65
## 28 178 0 4 2 2 3 read 47
## 29 196 0 4 3 2 2 read 44
## 30 29 0 2 1 1 1 read 52
## 31 126 0 4 2 1 1 read 42
## 32 103 0 4 3 1 2 read 76
## 33 192 0 4 3 2 2 read 65
## 34 150 0 4 2 1 3 read 42
## 35 199 0 4 3 2 2 read 52
## 36 144 0 4 3 1 1 read 60
## 37 200 0 4 2 2 2 read 68
## 38 80 0 4 3 1 2 read 65
## 39 16 0 1 1 1 3 read 47
## 40 153 0 4 2 1 3 read 39
## 41 176 0 4 2 2 2 read 47
## 42 177 0 4 2 2 2 read 55
## 43 168 0 4 2 1 2 read 52
## 44 40 0 3 1 1 1 read 42
## 45 62 0 4 3 1 1 read 65
## 46 169 0 4 1 1 1 read 55
## 47 49 0 3 3 1 3 read 50
## 48 136 0 4 2 1 2 read 65
## 49 189 0 4 2 2 2 read 47
## 50 7 0 1 2 1 2 read 57
## 51 27 0 2 2 1 2 read 53
## 52 128 0 4 3 1 2 read 39
## 53 21 0 1 2 1 1 read 44
## 54 183 0 4 2 2 2 read 63
## 55 132 0 4 2 1 2 read 73
## 56 15 0 1 3 1 3 read 39
## 57 67 0 4 1 1 3 read 37
## 58 22 0 1 2 1 3 read 42
## 59 185 0 4 2 2 2 read 63
## 60 9 0 1 2 1 3 read 48
## 61 181 0 4 2 2 2 read 50
## 62 170 0 4 3 1 2 read 47
## 63 134 0 4 1 1 1 read 44
## 64 108 0 4 2 1 1 read 34
## 65 197 0 4 3 2 2 read 50
## 66 140 0 4 2 1 3 read 44
## 67 171 0 4 2 1 2 read 60
## 68 107 0 4 1 1 3 read 47
## 69 81 0 4 1 1 2 read 63
## 70 18 0 1 2 1 3 read 50
## 71 155 0 4 2 1 1 read 44
## 72 97 0 4 3 1 2 read 60
## 73 68 0 4 2 1 2 read 73
## 74 157 0 4 2 1 1 read 68
## 75 56 0 4 2 1 3 read 55
## 76 5 0 1 1 1 2 read 47
## 77 159 0 4 3 1 2 read 55
## 78 123 0 4 3 1 1 read 68
## 79 164 0 4 2 1 3 read 31
## 80 14 0 1 3 1 2 read 47
## 81 127 0 4 3 1 2 read 63
## 82 165 0 4 1 1 3 read 36
## 83 174 0 4 2 2 2 read 68
## 84 3 0 1 1 1 2 read 63
## 85 58 0 4 2 1 3 read 55
## 86 146 0 4 3 1 2 read 55
## 87 102 0 4 3 1 2 read 52
## 88 117 0 4 3 1 3 read 34
## 89 133 0 4 2 1 3 read 50
## 90 94 0 4 3 1 2 read 55
## 91 24 0 2 2 1 2 read 52
## 92 149 0 4 1 1 1 read 63
## 93 82 1 4 3 1 2 read 68
## 94 8 1 1 1 1 2 read 39
## 95 129 1 4 1 1 1 read 44
## 96 173 1 4 1 1 1 read 50
## 97 57 1 4 2 1 2 read 71
## 98 100 1 4 3 1 2 read 63
## 99 1 1 1 1 1 3 read 34
## 100 194 1 4 3 2 2 read 63
## 101 88 1 4 3 1 2 read 68
## 102 99 1 4 3 1 1 read 47
## 103 47 1 3 1 1 2 read 47
## 104 120 1 4 3 1 2 read 63
## 105 166 1 4 2 1 2 read 52
## 106 65 1 4 2 1 2 read 55
## 107 101 1 4 3 1 2 read 60
## 108 89 1 4 1 1 3 read 35
## 109 54 1 3 1 2 1 read 47
## 110 180 1 4 3 2 2 read 71
## 111 162 1 4 2 1 3 read 57
## 112 4 1 1 1 1 2 read 44
## 113 131 1 4 3 1 2 read 65
## 114 125 1 4 1 1 2 read 68
## 115 34 1 1 3 2 2 read 73
## 116 106 1 4 2 1 3 read 36
## 117 130 1 4 3 1 1 read 43
## 118 93 1 4 3 1 2 read 73
## 119 163 1 4 1 1 2 read 52
## 120 37 1 3 1 1 3 read 41
## 121 35 1 1 1 2 1 read 60
## 122 87 1 4 2 1 1 read 50
## 123 73 1 4 2 1 2 read 50
## 124 151 1 4 2 1 3 read 47
## 125 44 1 3 1 1 3 read 47
## 126 152 1 4 3 1 2 read 55
## 127 105 1 4 2 1 2 read 50
## 128 28 1 2 2 1 1 read 39
## 129 91 1 4 3 1 3 read 50
## 130 45 1 3 1 1 3 read 34
## 131 116 1 4 2 1 2 read 57
## 132 33 1 2 1 1 2 read 57
## 133 66 1 4 2 1 3 read 68
## 134 72 1 4 2 1 3 read 42
## 135 77 1 4 1 1 2 read 61
## 136 61 1 4 3 1 2 read 76
## 137 190 1 4 2 2 2 read 47
## 138 42 1 3 2 1 3 read 46
## 139 2 1 1 2 1 3 read 39
## 140 55 1 3 2 2 2 read 52
## 141 19 1 1 1 1 1 read 28
## 142 90 1 4 3 1 2 read 42
## 143 142 1 4 2 1 3 read 47
## 144 17 1 1 2 1 2 read 47
## 145 122 1 4 2 1 2 read 52
## 146 191 1 4 3 2 2 read 47
## 147 83 1 4 2 1 3 read 50
## 148 182 1 4 2 2 2 read 44
## 149 6 1 1 1 1 2 read 47
## 150 46 1 3 1 1 2 read 45
## 151 43 1 3 1 1 2 read 47
## 152 96 1 4 3 1 2 read 65
## 153 138 1 4 2 1 3 read 43
## 154 10 1 1 2 1 1 read 47
## 155 71 1 4 2 1 1 read 57
## 156 139 1 4 2 1 2 read 68
## 157 110 1 4 2 1 3 read 52
## 158 148 1 4 2 1 3 read 42
## 159 109 1 4 2 1 1 read 42
## 160 39 1 3 3 1 2 read 66
## 161 147 1 4 1 1 2 read 47
## 162 74 1 4 2 1 2 read 57
## 163 198 1 4 3 2 2 read 47
## 164 161 1 4 1 1 2 read 57
## 165 112 1 4 2 1 2 read 52
## 166 69 1 4 1 1 3 read 44
## 167 156 1 4 2 1 2 read 50
## 168 111 1 4 1 1 1 read 39
## 169 186 1 4 2 2 2 read 57
## 170 98 1 4 1 1 3 read 57
## 171 119 1 4 1 1 1 read 42
## 172 13 1 1 2 1 3 read 47
## 173 51 1 3 3 1 1 read 42
## 174 26 1 2 3 1 2 read 60
## 175 36 1 3 1 1 1 read 44
## 176 135 1 4 1 1 2 read 63
## 177 59 1 4 2 1 2 read 65
## 178 78 1 4 2 1 2 read 39
## 179 64 1 4 3 1 3 read 50
## 180 63 1 4 1 1 1 read 52
## 181 79 1 4 2 1 2 read 60
## 182 193 1 4 2 2 2 read 44
## 183 92 1 4 3 1 1 read 52
## 184 160 1 4 2 1 2 read 55
## 185 32 1 2 3 1 3 read 50
## 186 23 1 2 1 1 2 read 65
## 187 158 1 4 2 1 1 read 52
## 188 25 1 2 2 1 1 read 47
## 189 188 1 4 3 2 2 read 63
## 190 52 1 3 1 1 2 read 50
## 191 124 1 4 1 1 3 read 42
## 192 175 1 4 3 2 1 read 36
## 193 184 1 4 2 2 3 read 50
## 194 30 1 2 3 1 2 read 41
## 195 179 1 4 2 2 2 read 47
## 196 31 1 2 2 2 1 read 55
## 197 145 1 4 2 1 3 read 42
## 198 187 1 4 2 2 1 read 57
## 199 118 1 4 2 1 1 read 55
## 200 137 1 4 3 1 2 read 63
## 201 70 0 4 1 1 1 write 52
## 202 121 1 4 2 1 3 write 59
## 203 86 0 4 3 1 1 write 33
## 204 141 0 4 3 1 3 write 44
## 205 172 0 4 2 1 2 write 52
## 206 113 0 4 2 1 2 write 52
## 207 50 0 3 2 1 1 write 59
## 208 11 0 1 2 1 2 write 46
## 209 84 0 4 2 1 1 write 57
## 210 48 0 3 2 1 2 write 55
## 211 75 0 4 2 1 3 write 46
## 212 60 0 4 2 1 2 write 65
## 213 95 0 4 3 1 2 write 60
## 214 104 0 4 3 1 2 write 63
## 215 38 0 3 1 1 2 write 57
## 216 115 0 4 1 1 1 write 49
## 217 76 0 4 3 1 2 write 52
## 218 195 0 4 2 2 1 write 57
## 219 114 0 4 3 1 2 write 65
## 220 85 0 4 2 1 1 write 39
## 221 167 0 4 2 1 1 write 49
## 222 143 0 4 2 1 3 write 63
## 223 41 0 3 2 1 2 write 40
## 224 20 0 1 3 1 2 write 52
## 225 12 0 1 2 1 3 write 44
## 226 53 0 3 2 1 3 write 37
## 227 154 0 4 3 1 2 write 65
## 228 178 0 4 2 2 3 write 57
## 229 196 0 4 3 2 2 write 38
## 230 29 0 2 1 1 1 write 44
## 231 126 0 4 2 1 1 write 31
## 232 103 0 4 3 1 2 write 52
## 233 192 0 4 3 2 2 write 67
## 234 150 0 4 2 1 3 write 41
## 235 199 0 4 3 2 2 write 59
## 236 144 0 4 3 1 1 write 65
## 237 200 0 4 2 2 2 write 54
## 238 80 0 4 3 1 2 write 62
## 239 16 0 1 1 1 3 write 31
## 240 153 0 4 2 1 3 write 31
## 241 176 0 4 2 2 2 write 47
## 242 177 0 4 2 2 2 write 59
## 243 168 0 4 2 1 2 write 54
## 244 40 0 3 1 1 1 write 41
## 245 62 0 4 3 1 1 write 65
## 246 169 0 4 1 1 1 write 59
## 247 49 0 3 3 1 3 write 40
## 248 136 0 4 2 1 2 write 59
## 249 189 0 4 2 2 2 write 59
## 250 7 0 1 2 1 2 write 54
## 251 27 0 2 2 1 2 write 61
## 252 128 0 4 3 1 2 write 33
## 253 21 0 1 2 1 1 write 44
## 254 183 0 4 2 2 2 write 59
## 255 132 0 4 2 1 2 write 62
## 256 15 0 1 3 1 3 write 39
## 257 67 0 4 1 1 3 write 37
## 258 22 0 1 2 1 3 write 39
## 259 185 0 4 2 2 2 write 57
## 260 9 0 1 2 1 3 write 49
## 261 181 0 4 2 2 2 write 46
## 262 170 0 4 3 1 2 write 62
## 263 134 0 4 1 1 1 write 44
## 264 108 0 4 2 1 1 write 33
## 265 197 0 4 3 2 2 write 42
## 266 140 0 4 2 1 3 write 41
## 267 171 0 4 2 1 2 write 54
## 268 107 0 4 1 1 3 write 39
## 269 81 0 4 1 1 2 write 43
## 270 18 0 1 2 1 3 write 33
## 271 155 0 4 2 1 1 write 44
## 272 97 0 4 3 1 2 write 54
## 273 68 0 4 2 1 2 write 67
## 274 157 0 4 2 1 1 write 59
## 275 56 0 4 2 1 3 write 45
## 276 5 0 1 1 1 2 write 40
## 277 159 0 4 3 1 2 write 61
## 278 123 0 4 3 1 1 write 59
## 279 164 0 4 2 1 3 write 36
## 280 14 0 1 3 1 2 write 41
## 281 127 0 4 3 1 2 write 59
## 282 165 0 4 1 1 3 write 49
## 283 174 0 4 2 2 2 write 59
## 284 3 0 1 1 1 2 write 65
## 285 58 0 4 2 1 3 write 41
## 286 146 0 4 3 1 2 write 62
## 287 102 0 4 3 1 2 write 41
## 288 117 0 4 3 1 3 write 49
## 289 133 0 4 2 1 3 write 31
## 290 94 0 4 3 1 2 write 49
## 291 24 0 2 2 1 2 write 62
## 292 149 0 4 1 1 1 write 49
## 293 82 1 4 3 1 2 write 62
## 294 8 1 1 1 1 2 write 44
## 295 129 1 4 1 1 1 write 44
## 296 173 1 4 1 1 1 write 62
## 297 57 1 4 2 1 2 write 65
## 298 100 1 4 3 1 2 write 65
## 299 1 1 1 1 1 3 write 44
## 300 194 1 4 3 2 2 write 63
## 301 88 1 4 3 1 2 write 60
## 302 99 1 4 3 1 1 write 59
## 303 47 1 3 1 1 2 write 46
## 304 120 1 4 3 1 2 write 52
## 305 166 1 4 2 1 2 write 59
## 306 65 1 4 2 1 2 write 54
## 307 101 1 4 3 1 2 write 62
## 308 89 1 4 1 1 3 write 35
## 309 54 1 3 1 2 1 write 54
## 310 180 1 4 3 2 2 write 65
## 311 162 1 4 2 1 3 write 52
## 312 4 1 1 1 1 2 write 50
## 313 131 1 4 3 1 2 write 59
## 314 125 1 4 1 1 2 write 65
## 315 34 1 1 3 2 2 write 61
## 316 106 1 4 2 1 3 write 44
## 317 130 1 4 3 1 1 write 54
## 318 93 1 4 3 1 2 write 67
## 319 163 1 4 1 1 2 write 57
## 320 37 1 3 1 1 3 write 47
## 321 35 1 1 1 2 1 write 54
## 322 87 1 4 2 1 1 write 52
## 323 73 1 4 2 1 2 write 52
## 324 151 1 4 2 1 3 write 46
## 325 44 1 3 1 1 3 write 62
## 326 152 1 4 3 1 2 write 57
## 327 105 1 4 2 1 2 write 41
## 328 28 1 2 2 1 1 write 53
## 329 91 1 4 3 1 3 write 49
## 330 45 1 3 1 1 3 write 35
## 331 116 1 4 2 1 2 write 59
## 332 33 1 2 1 1 2 write 65
## 333 66 1 4 2 1 3 write 62
## 334 72 1 4 2 1 3 write 54
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## 336 61 1 4 3 1 2 write 63
## 337 190 1 4 2 2 2 write 59
## 338 42 1 3 2 1 3 write 52
## 339 2 1 1 2 1 3 write 41
## 340 55 1 3 2 2 2 write 49
## 341 19 1 1 1 1 1 write 46
## 342 90 1 4 3 1 2 write 54
## 343 142 1 4 2 1 3 write 42
## 344 17 1 1 2 1 2 write 57
## 345 122 1 4 2 1 2 write 59
## 346 191 1 4 3 2 2 write 52
## 347 83 1 4 2 1 3 write 62
## 348 182 1 4 2 2 2 write 52
## 349 6 1 1 1 1 2 write 41
## 350 46 1 3 1 1 2 write 55
## 351 43 1 3 1 1 2 write 37
## 352 96 1 4 3 1 2 write 54
## 353 138 1 4 2 1 3 write 57
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## 356 139 1 4 2 1 2 write 59
## 357 110 1 4 2 1 3 write 55
## 358 148 1 4 2 1 3 write 57
## 359 109 1 4 2 1 1 write 39
## 360 39 1 3 3 1 2 write 67
## 361 147 1 4 1 1 2 write 62
## 362 74 1 4 2 1 2 write 50
## 363 198 1 4 3 2 2 write 61
## 364 161 1 4 1 1 2 write 62
## 365 112 1 4 2 1 2 write 59
## 366 69 1 4 1 1 3 write 44
## 367 156 1 4 2 1 2 write 59
## 368 111 1 4 1 1 1 write 54
## 369 186 1 4 2 2 2 write 62
## 370 98 1 4 1 1 3 write 60
## 371 119 1 4 1 1 1 write 57
## 372 13 1 1 2 1 3 write 46
## 373 51 1 3 3 1 1 write 36
## 374 26 1 2 3 1 2 write 59
## 375 36 1 3 1 1 1 write 49
## 376 135 1 4 1 1 2 write 60
## 377 59 1 4 2 1 2 write 67
## 378 78 1 4 2 1 2 write 54
## 379 64 1 4 3 1 3 write 52
## 380 63 1 4 1 1 1 write 65
## 381 79 1 4 2 1 2 write 62
## 382 193 1 4 2 2 2 write 49
## 383 92 1 4 3 1 1 write 67
## 384 160 1 4 2 1 2 write 65
## 385 32 1 2 3 1 3 write 67
## 386 23 1 2 1 1 2 write 65
## 387 158 1 4 2 1 1 write 54
## 388 25 1 2 2 1 1 write 44
## 389 188 1 4 3 2 2 write 62
## 390 52 1 3 1 1 2 write 46
## 391 124 1 4 1 1 3 write 54
## 392 175 1 4 3 2 1 write 57
## 393 184 1 4 2 2 3 write 52
## 394 30 1 2 3 1 2 write 59
## 395 179 1 4 2 2 2 write 65
## 396 31 1 2 2 2 1 write 59
## 397 145 1 4 2 1 3 write 46
## 398 187 1 4 2 2 1 write 41
## 399 118 1 4 2 1 1 write 62
## 400 137 1 4 3 1 2 write 65
## 401 70 0 4 1 1 1 math 41
## 402 121 1 4 2 1 3 math 53
## 403 86 0 4 3 1 1 math 54
## 404 141 0 4 3 1 3 math 47
## 405 172 0 4 2 1 2 math 57
## 406 113 0 4 2 1 2 math 51
## 407 50 0 3 2 1 1 math 42
## 408 11 0 1 2 1 2 math 45
## 409 84 0 4 2 1 1 math 54
## 410 48 0 3 2 1 2 math 52
## 411 75 0 4 2 1 3 math 51
## 412 60 0 4 2 1 2 math 51
## 413 95 0 4 3 1 2 math 71
## 414 104 0 4 3 1 2 math 57
## 415 38 0 3 1 1 2 math 50
## 416 115 0 4 1 1 1 math 43
## 417 76 0 4 3 1 2 math 51
## 418 195 0 4 2 2 1 math 60
## 419 114 0 4 3 1 2 math 62
## 420 85 0 4 2 1 1 math 57
## 421 167 0 4 2 1 1 math 35
## 422 143 0 4 2 1 3 math 75
## 423 41 0 3 2 1 2 math 45
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library(readr)
hsb2 <- read_csv("files/hsb2.csv")
## New names:
## Rows: 200 Columns: 12
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," dbl
## (12): ...1, id, female, race, ses, schtyp, prog, read, write, math, scie...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
#Remark: Pay extra attention to the last 2 columns
head(hsb2_long)
## id female race ses schtyp prog variable value
## 1 70 0 4 1 1 1 read 57
## 2 121 1 4 2 1 3 read 68
## 3 86 0 4 3 1 1 read 44
## 4 141 0 4 3 1 3 read 63
## 5 172 0 4 2 1 2 read 47
## 6 113 0 4 2 1 2 read 44
tail(hsb2_long)
## id female race ses schtyp prog variable value
## 995 179 1 4 2 2 2 socst 56
## 996 31 1 2 2 2 1 socst 56
## 997 145 1 4 2 1 3 socst 46
## 998 187 1 4 2 2 1 socst 52
## 999 118 1 4 2 1 1 socst 61
## 1000 137 1 4 3 1 2 socst 61
# get thefrequency
table(hsb2_long$variable)
##
## read write math science socst
## 200 200 200 200 200
# This means that the tables hsb2_long and hsb2_wide are the same
# tables displayed in two ways
# display data structure of the hsb2_long dataset
str(hsb2_long)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : num 70 121 86 141 172 113 50 11 84 48 ...
## $ female : num 0 1 0 0 0 0 0 0 0 0 ...
## $ race : num 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : num 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : num 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : num 1 3 1 3 2 2 1 2 1 2 ...
## $ variable: Factor w/ 5 levels "read","write",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : num 57 68 44 63 47 44 50 34 63 57 ...
# the variables female, race, ses, schtyp, prog are stored as numbers
# for encoding purposes. However these variables are actually qualitative variables
# so we convert each from integer type to categorical type
# defining some variables to become factor variable
# we use another variable to preserve the file hsb2_long
data <- hsb2_long
data$ses = factor(data$ses, labels=c("low", "middle", "high"))
data$schtyp = factor(data$schtyp, labels=c("public", "private"))
data$prog = factor(data$prog, labels=c("general", "academic", "vocational"))
data$race = factor(data$race, labels=c("hispanic", "asian", "african-
amer","white"))
data$female = factor(data$female, labels=c("female", "male"))
# check data structure again. The former integer variables are now categorical variable
str(data)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : num 70 121 86 141 172 113 50 11 84 48 ...
## $ female : Factor w/ 2 levels "female","male": 1 2 1 1 1 1 1 1 1 1 ...
## $ race : Factor w/ 4 levels "hispanic","asian",..: 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : Factor w/ 3 levels "low","middle",..: 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : Factor w/ 2 levels "public","private": 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : Factor w/ 3 levels "general","academic",..: 1 3 1 3 2 2 1 2 1 2 ...
## $ variable: Factor w/ 5 levels "read","write",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : num 57 68 44 63 47 44 50 34 63 57 ...
# we compare student performance by using boxplots
library(gplots)
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess
# compute the average value by group using tapply() command
means <- round(tapply(data$value, data$variable, mean), digits=2)
# create boxplot by group
boxplot(data$value ~ data$variable, main= "Student Performance by Subject
(brown dot = mean score)",
xlab="Subject matter", ylab="Percentage Scores", col=rainbow(5))
# insert the average values
points(means, col="brown", pch=18)
# can also compute the median values
medians = round(tapply(data$value, data$variable, median), digits=2)
medians
## read write math science socst
## 50 54 52 53 52
points(medians, col="red", pch=18)
# Lab Exercise 9: How to plot categorical variables
library(ggplot2)
# we load variable names to memory to avoid the dollar notation
attach(hsb2_long)
# create the plot object p
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~race)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
library(ggplot2)
attach(hsb2_long)
## The following objects are masked from hsb2_long (pos = 3):
##
## female, id, prog, race, schtyp, ses, value, variable
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~schtyp)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
# Lab Exercise 10: Scatter Plots with marginal Distributions
# Advance Scatter plots using libraries
# install.packages("ggExtra")
# install.packages("tidyverse")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ stringr 1.5.0
## ✔ forcats 1.0.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggExtra)
# set theme appearance of grid background
theme_set(theme_bw(1))
# create X and Y vector
(xAxis <- rnorm(1000)) # normal distribution with mean 0 and sd=1
## [1] 1.587920e+00 -1.722684e-01 1.739227e-01 -9.073087e-01 1.092217e+00
## [6] -2.492513e+00 -2.658749e-01 -1.983148e+00 -1.569309e-01 -1.529130e+00
## [11] -7.915475e-01 7.357725e-01 2.035541e-01 6.193544e-01 5.212013e-01
## [16] -4.698678e-01 -4.777037e-01 -1.006837e+00 -1.144046e-01 -5.595166e-01
## [21] -1.326968e-01 -1.469577e-01 1.623082e-01 -4.025620e-01 -4.825773e-01
## [26] 1.575530e+00 2.314619e-02 1.841717e+00 1.141920e+00 7.576980e-01
## [31] -1.121785e-01 5.612200e-01 2.396383e+00 -5.824677e-01 -2.166031e+00
## [36] -4.547742e-01 2.624247e+00 -8.974293e-01 1.197871e-01 -5.985503e-01
## [41] 4.043213e-01 3.786685e-01 6.295023e-01 2.018307e+00 6.157220e-01
## [46] 9.800435e-01 5.662733e-01 1.449137e+00 2.179945e-01 2.424981e-01
## [51] 4.917759e-02 -6.647182e-01 -7.355422e-01 -5.115664e-01 3.099128e-01
## [56] 2.965093e-01 -1.006876e+00 7.842893e-01 -6.445985e-01 -1.095109e-01
## [61] -3.450017e-01 4.524661e-01 -2.148408e-01 -1.262717e+00 -4.017046e-01
## [66] -1.322333e+00 -8.591730e-01 -3.602382e-01 7.131889e-02 1.157999e+00
## [71] -4.894134e-01 1.736630e+00 -1.424746e-02 -1.053363e-01 6.175860e-01
## [76] -1.609722e+00 2.405156e-01 -4.451738e-01 -4.250929e-01 6.646878e-01
## [81] 1.238249e+00 -7.737099e-01 7.781273e-01 7.892664e-01 1.209510e+00
## [86] 9.856757e-01 1.218858e+00 4.715056e-01 4.109596e-01 -8.660229e-01
## [91] -4.768793e-01 1.579949e+00 4.744178e-01 -8.050216e-01 -8.215773e-01
## [96] -1.324710e-01 -1.124988e+00 1.892529e-01 3.517427e-01 -1.151522e-01
## [101] 2.393821e+00 7.954308e-01 -1.442011e-01 2.093998e+00 -1.237386e+00
## [106] 8.019452e-02 -8.551382e-01 1.154670e+00 6.630603e-01 1.020724e+00
## [111] 8.666457e-04 -1.551402e+00 -3.246046e-01 9.575904e-01 -5.042361e-01
## [116] -3.381010e-01 1.738801e+00 7.979930e-01 -1.833400e+00 -5.311788e-01
## [121] -3.850130e-01 8.476324e-02 4.959785e-01 -9.061115e-02 9.362350e-02
## [126] -1.163593e+00 -2.406275e-02 -1.387455e+00 -1.004692e+00 -1.032002e+00
## [131] -2.069973e-01 3.614803e-01 6.291514e-01 2.012165e-01 4.968673e-01
## [136] -1.183342e-01 5.005629e-01 -1.205854e+00 -1.421657e-01 -2.034007e+00
## [141] 4.231552e-01 2.294028e-01 8.328690e-01 -5.386442e-01 -7.319092e-01
## [146] -6.466631e-01 -1.976410e-01 -8.517918e-01 5.554984e-02 6.043288e-01
## [151] -8.353317e-01 4.876827e-01 1.309247e+00 8.990664e-02 7.229827e-01
## [156] -2.076048e+00 6.287588e-02 5.656863e-01 -9.701690e-02 3.679533e-01
## [161] 1.770489e+00 1.897416e-01 1.346829e+00 -4.708850e-01 7.339856e-01
## [166] -2.737404e+00 -1.439972e+00 1.363184e+00 1.314152e+00 3.917118e-01
## [171] 8.432119e-01 6.795898e-01 -1.388424e+00 1.933951e+00 6.189742e-01
## [176] -1.063640e+00 -8.480410e-01 -7.392480e-01 6.982161e-01 -9.030033e-01
## [181] -6.119994e-02 -1.567804e+00 4.692320e-01 3.727023e-01 1.153730e+00
## [186] 1.351707e+00 1.825505e-01 5.648123e-01 2.252145e+00 -1.318443e+00
## [191] -1.484874e+00 -3.132031e-01 3.917951e-01 -5.223503e-01 -1.308493e+00
## [196] 1.923254e+00 2.082199e+00 2.500971e-01 -1.603403e+00 -4.203267e-01
## [201] 7.512202e-02 1.459077e-01 5.361882e-01 -7.550430e-01 1.231330e+00
## [206] 2.283683e-01 2.635353e-01 -6.494000e-01 -1.708576e-01 1.550781e-01
## [211] -8.159940e-01 -1.888456e-01 3.672309e-01 3.036284e-01 -1.189241e-01
## [216] 4.626075e-01 -2.751402e-03 2.713570e-01 -2.605269e-01 -2.221418e-01
## [221] 4.457677e-01 1.696281e+00 3.752946e-01 7.353636e-01 -1.237369e+00
## [226] -1.253690e+00 5.386757e-01 -1.311890e+00 1.456827e+00 -4.190606e-01
## [231] -1.014613e+00 6.227033e-01 -3.857563e-01 -5.775524e-01 -4.829732e-01
## [236] -1.424377e+00 -1.673106e+00 8.894994e-01 1.393524e+00 6.901717e-01
## [241] 8.683768e-01 1.041279e+00 -5.090490e-01 1.502113e+00 1.020895e+00
## [246] 8.157094e-01 -6.540571e-01 8.572644e-01 8.378394e-01 7.798819e-01
## [251] -7.527332e-01 1.118876e+00 2.166046e+00 5.810223e-01 3.827395e-01
## [256] 8.871889e-01 -4.156320e-01 -1.203535e+00 6.086916e-01 6.494979e-01
## [261] -2.540508e-01 7.188356e-01 4.650806e-01 -9.542081e-01 -1.202039e+00
## [266] -1.955842e+00 1.748330e+00 -9.600780e-01 1.309636e+00 2.063876e-01
## [271] 4.872189e-01 -8.300234e-01 -4.302574e-01 -8.390864e-01 -2.043572e-01
## [276] -7.685188e-01 -9.179052e-01 -4.296205e-01 -9.687062e-01 -2.101671e+00
## [281] -1.113589e-01 1.295357e+00 1.236836e+00 1.928263e-01 3.136773e-01
## [286] -2.571475e+00 -9.616608e-01 4.070666e-01 1.804002e-01 4.993185e-01
## [291] -7.511410e-02 5.842219e-01 -1.070416e+00 -7.710947e-01 1.141717e+00
## [296] -2.139739e+00 1.051565e+00 -6.826369e-02 -2.276987e+00 -6.067323e-01
## [301] -7.855414e-01 3.806401e-01 -8.081830e-01 1.050269e+00 1.074957e-02
## [306] 6.144769e-01 1.242727e+00 -1.004271e+00 -9.487057e-01 -1.208176e+00
## [311] -1.171731e+00 -1.080440e+00 -1.043333e+00 1.163805e-01 1.665792e+00
## [316] 1.447482e+00 -1.677617e+00 1.355175e+00 9.820998e-01 5.204716e-01
## [321] -1.177960e+00 1.426903e+00 6.972220e-01 -1.261489e+00 -1.250603e+00
## [326] -8.761825e-01 -3.653293e-01 -6.655556e-01 -3.725246e-01 7.780749e-02
## [331] -6.962576e-01 1.023039e+00 2.385984e-01 5.206476e-01 -3.549041e-01
## [336] 5.988435e-01 7.646724e-01 1.790202e+00 4.559988e-01 -2.214319e-01
## [341] 1.199537e+00 -1.521541e+00 9.326165e-01 1.178065e+00 3.399747e-01
## [346] -6.567897e-01 8.862869e-01 -7.641756e-01 9.170380e-02 -6.698229e-01
## [351] -6.937477e-01 -2.021864e+00 1.358977e+00 -1.397386e+00 7.701506e-01
## [356] -6.786974e-01 4.688485e-01 -9.018908e-01 -4.296670e-01 3.393110e-01
## [361] -8.144784e-01 5.052416e-02 -2.584927e-01 -1.403037e+00 1.037124e+00
## [366] 6.276008e-01 7.380609e-01 -1.355911e+00 -5.777747e-02 -3.114570e-01
## [371] 4.366156e-01 2.822506e-01 4.153534e-01 -6.927618e-01 2.314731e-01
## [376] 1.486319e-01 -1.104513e+00 -2.954959e-01 2.257242e-01 -7.397352e-01
## [381] -2.014193e+00 1.300619e+00 -1.407738e+00 1.719300e+00 7.088482e-02
## [386] -4.335020e-01 -1.340300e+00 1.612962e-01 5.954215e-01 -6.507162e-01
## [391] 2.679015e-01 6.368845e-01 4.772100e-01 -3.123329e-01 -5.897703e-01
## [396] 7.854413e-01 1.275706e+00 1.775983e-01 1.190427e+00 -1.006629e+00
## [401] -2.253115e+00 -2.710180e-01 2.660507e-01 -1.166923e+00 -2.889998e-01
## [406] 1.695463e-01 -4.329863e-01 9.721005e-01 -2.026829e-01 -1.019386e+00
## [411] 1.460996e+00 5.016765e-01 4.164739e-01 5.261165e-01 -1.309019e+00
## [416] -1.116773e-01 -4.393346e-01 -3.816180e-01 -3.287046e-01 1.190312e+00
## [421] 7.069153e-01 2.255733e+00 1.347851e+00 -1.299155e+00 -5.640608e-01
## [426] -2.044972e+00 -4.175260e-01 -2.526593e-01 -8.967489e-01 1.188883e+00
## [431] -1.545875e-01 -1.783447e+00 2.041462e+00 -4.057847e-01 -1.053198e+00
## [436] -1.107032e-01 5.425271e-01 -1.197815e+00 -2.004802e+00 1.842444e-01
## [441] 1.348885e-01 2.526817e-01 1.166934e+00 -9.459504e-01 1.112717e+00
## [446] -5.419582e-01 -4.414428e-01 -4.052473e-03 -1.068742e+00 6.532021e-01
## [451] 2.238384e-01 1.154699e+00 1.433123e+00 -1.337020e+00 4.622788e-01
## [456] 6.382277e-01 -3.613751e-01 9.853963e-01 6.911597e-01 -8.344802e-01
## [461] 1.548869e-01 -1.095792e-01 4.735023e-01 -9.382432e-01 -1.819842e+00
## [466] -7.404112e-01 -1.752297e+00 5.804744e-01 -1.208336e+00 3.063091e-01
## [471] -1.062633e+00 2.971290e-01 7.552643e-01 -4.631582e-01 2.224515e-01
## [476] 1.264256e+00 -1.494961e+00 -5.825505e-02 -1.327722e+00 1.282183e+00
## [481] -4.025265e-01 -4.334305e-02 -2.809226e-01 3.194257e-01 -9.016231e-01
## [486] -3.953599e-01 -1.244410e+00 2.744827e-01 1.320680e-02 9.763440e-01
## [491] 1.235548e+00 4.661964e-02 2.943566e-01 4.265324e-02 -9.043202e-02
## [496] 1.334373e+00 2.253190e-01 7.874351e-01 3.588021e-01 -1.804968e+00
## [501] 3.023324e-01 -2.760986e-01 1.074364e+00 -3.902785e-01 -1.840134e+00
## [506] 6.417341e-02 -6.321286e-02 -9.494583e-01 3.320812e-01 1.442835e+00
## [511] 5.459025e-01 -9.408383e-01 1.427490e+00 7.991998e-02 8.448521e-01
## [516] -1.770719e+00 2.400379e+00 1.017199e+00 -6.792368e-01 4.350424e-01
## [521] 3.154344e-01 8.421561e-01 4.057257e-01 1.236673e+00 -2.149587e+00
## [526] -1.963326e+00 7.581834e-01 5.606442e-01 2.837747e-01 -2.177778e+00
## [531] -1.303607e+00 -1.546795e-01 2.401308e+00 -1.092800e-01 5.287177e-01
## [536] 1.049573e+00 -6.758937e-01 1.405475e-02 7.318892e-01 1.349366e-01
## [541] -8.714640e-01 6.232904e-01 -8.011786e-01 1.813640e+00 1.080708e+00
## [546] -7.665278e-01 9.584773e-01 -2.133968e-01 9.513218e-01 -1.782608e-01
## [551] -6.021844e-01 1.628400e+00 1.218155e-01 5.344282e-01 9.045496e-01
## [556] -8.839628e-01 -2.557967e+00 2.227381e+00 -1.321629e+00 -1.088787e+00
## [561] -1.565141e+00 -1.453535e+00 6.319854e-01 -7.809648e-01 1.522945e+00
## [566] 9.029934e-02 -2.797284e-01 4.185699e-01 -1.585899e-01 -1.792403e-01
## [571] 1.486680e+00 -2.686903e-01 -1.394359e+00 -7.698163e-01 -1.820830e+00
## [576] 1.266016e-01 1.127257e+00 -9.182546e-01 -8.871931e-01 9.841554e-01
## [581] 2.557485e-01 -2.326206e+00 -7.965680e-01 1.831275e-01 6.725072e-01
## [586] 6.964315e-01 2.165534e-01 -6.206525e-01 -3.050678e-01 1.510169e+00
## [591] 4.539191e-01 -1.017537e+00 -8.267657e-01 3.799883e-01 4.092645e-01
## [596] 1.225345e+00 2.189696e+00 -1.189546e-01 -6.773289e-02 3.367233e-01
## [601] 2.377913e-01 1.153212e-01 -2.399492e+00 7.925335e-02 2.405997e-02
## [606] -1.802921e+00 1.339116e+00 -4.122206e-02 6.998854e-01 2.522495e-01
## [611] -9.829355e-01 -1.299466e+00 5.569971e-01 2.670161e-02 -9.655104e-01
## [616] -2.849972e+00 -3.988447e-01 -6.313206e-01 1.191732e-01 -9.634025e-02
## [621] -9.873734e-01 -7.102051e-01 -1.390819e+00 -4.806562e-01 -1.631363e+00
## [626] -5.792961e-01 -1.408615e+00 1.321824e-02 -2.289589e-01 -1.364367e+00
## [631] 1.609274e+00 -6.719274e-02 -9.753534e-01 -1.027597e+00 1.165179e+00
## [636] -1.792818e+00 3.158730e-02 -1.840595e+00 -2.442885e-01 -1.225801e-01
## [641] 6.023907e-01 1.846469e-01 8.617494e-02 5.454921e-01 -5.047661e-01
## [646] 8.603903e-01 -1.191987e+00 1.229764e+00 1.421339e-01 -5.928203e-01
## [651] 3.598239e-01 -1.316635e+00 -7.101537e-01 9.490581e-01 3.643707e-01
## [656] 5.789703e-01 1.894122e+00 8.475010e-01 8.068502e-01 -1.181573e-01
## [661] 8.068468e-01 -1.460556e-01 -5.358690e-01 -4.469229e-01 -1.163796e+00
## [666] -1.342720e+00 1.874075e+00 5.850382e-01 1.526727e-01 -3.501679e-01
## [671] -2.542741e-01 -3.590981e-01 -5.481337e-01 6.353007e-01 1.313162e+00
## [676] -1.612170e+00 1.678656e-01 -2.252613e-02 -5.774876e-01 -2.523652e-01
## [681] 1.258481e+00 1.049350e+00 6.727288e-01 -4.412843e-01 3.881518e-01
## [686] -1.076975e+00 -8.467576e-01 -1.439741e+00 -4.386674e-01 -6.845034e-02
## [691] 1.247991e+00 4.389307e-01 1.744021e-02 8.671408e-01 1.067637e+00
## [696] -1.961215e-01 -1.498702e+00 -4.412995e-05 -1.408235e-02 -1.125704e-01
## [701] 5.147691e-01 -4.011832e-01 1.437481e+00 1.478325e-01 -1.992783e+00
## [706] 3.664397e-01 -6.420429e-01 3.293169e-01 7.009921e-01 -6.206347e-01
## [711] -1.724955e+00 1.966063e+00 -1.378130e+00 -2.096398e+00 8.709066e-01
## [716] -3.347134e-01 -2.706323e+00 -7.757772e-01 6.849135e-01 7.488897e-01
## [721] 9.017515e-01 -2.120076e+00 -6.219932e-01 1.080956e+00 1.550415e+00
## [726] 1.083739e+00 2.932667e-01 -1.901664e+00 2.373534e+00 -9.291860e-01
## [731] 2.171234e-01 -1.712410e-01 1.782172e+00 -2.207666e+00 -7.860882e-01
## [736] 1.465355e+00 -2.624971e-01 6.733573e-02 5.697707e-02 -1.930726e-01
## [741] -3.901339e-01 -7.745444e-01 7.834643e-01 3.731615e-01 9.211562e-01
## [746] 5.089627e-01 9.950958e-02 1.227317e+00 2.197760e-01 6.293086e-01
## [751] 1.422318e+00 -9.589117e-02 -9.656918e-02 -5.096619e-01 7.086683e-01
## [756] -7.708509e-01 2.129448e+00 2.104802e-01 1.790848e+00 -5.181278e-01
## [761] -1.720068e-01 -8.867273e-01 -9.183296e-01 -2.298048e-01 1.871784e-01
## [766] 1.005625e-03 -1.222135e-01 6.470755e-01 -7.919465e-01 1.324825e+00
## [771] -1.061270e-01 1.512414e-01 -6.931698e-01 -7.367563e-01 -4.336118e-01
## [776] -1.385414e+00 4.811309e-01 -6.752681e-02 5.056360e-01 -8.011701e-02
## [781] 6.497062e-02 -1.197788e+00 1.217554e+00 -1.008586e+00 1.179052e+00
## [786] 4.970738e-01 1.049546e+00 -6.820005e-01 8.855230e-01 -7.892403e-03
## [791] 4.232710e-02 1.309521e+00 1.725715e+00 -1.281865e-02 3.621396e-01
## [796] -3.922082e-02 1.874299e+00 -1.260730e+00 3.609923e-01 -6.553099e-02
## [801] 3.925885e-02 -7.834902e-01 -1.322833e+00 1.413263e+00 -7.180274e-02
## [806] 8.343967e-02 -2.522373e+00 -4.592799e-02 3.324277e-01 -1.827789e-01
## [811] -1.270345e+00 -5.467073e-02 -9.018603e-01 1.356938e+00 -3.265134e+00
## [816] -7.474620e-01 -1.539634e+00 3.966849e-02 1.722299e+00 -3.324001e-01
## [821] 3.103110e-01 1.159218e+00 -4.528714e-01 -1.286412e-01 1.128268e+00
## [826] 3.261200e-01 5.235325e-01 -8.613885e-01 2.303658e+00 4.215063e-01
## [831] 6.404882e-01 3.974760e-01 4.710888e-01 -1.350545e+00 5.297218e-01
## [836] -3.038522e-01 -1.080807e+00 -5.563953e-02 -1.085609e-01 5.341072e-01
## [841] -1.297302e+00 -1.702463e-01 -1.141634e+00 -5.331321e-01 2.756679e-01
## [846] 8.440601e-01 -5.661866e-01 -1.307921e+00 -5.586357e-01 1.333560e+00
## [851] 5.261104e-01 4.791047e-01 -3.548517e-01 1.249250e+00 -7.171962e-01
## [856] -1.248341e+00 2.448731e+00 -9.274024e-01 2.235744e+00 7.128474e-01
## [861] -1.413706e+00 1.605148e+00 1.427332e+00 3.914653e-01 -2.271177e-01
## [866] -1.023709e+00 3.535959e-01 -1.174833e+00 1.517896e+00 7.629983e-01
## [871] -2.510879e-01 -2.579189e-01 -1.917327e+00 3.310220e-01 -9.235488e-01
## [876] -1.545510e-02 -2.275592e+00 -9.938606e-01 2.082797e-01 -6.429848e-01
## [881] 2.964704e-01 -7.017275e-01 1.217546e+00 -1.363014e+00 1.287549e+00
## [886] -3.590104e-01 1.420157e+00 6.103295e-01 -1.196685e+00 9.884190e-01
## [891] 5.963686e-01 -6.004858e-01 -7.175605e-01 4.783243e-01 2.407407e+00
## [896] 2.281850e-01 1.317716e+00 -4.068819e-01 3.327106e-01 1.365657e-01
## [901] 1.660959e+00 -5.759613e-01 2.529256e-01 -2.364811e-01 -6.756963e-01
## [906] 5.423077e-01 5.882883e-01 1.411416e+00 -1.194420e+00 1.129333e+00
## [911] 9.025991e-02 1.358759e+00 -1.020273e-01 -3.893023e-01 1.936781e-01
## [916] 4.086870e-01 4.477241e-01 -1.821216e+00 8.730719e-01 2.885244e-01
## [921] -7.462940e-01 9.523097e-01 -2.876205e+00 1.118481e+00 -2.196460e+00
## [926] -7.733950e-01 -6.300549e-01 -7.529747e-01 5.432819e-01 1.314132e-01
## [931] 8.849305e-01 2.066789e-01 -1.006989e+00 -8.160521e-02 1.640901e+00
## [936] 3.742038e-01 5.854166e-01 2.390587e-01 -2.295413e-01 -9.629658e-01
## [941] -2.255773e-02 -5.195199e-01 -4.282264e-02 4.629206e-01 -7.719536e-01
## [946] -1.170939e+00 2.049763e+00 1.540887e+00 5.195690e-01 4.967553e-01
## [951] 2.396223e-01 -1.666823e+00 -2.187899e+00 1.547943e+00 -2.667950e-01
## [956] 7.846768e-01 -2.749822e-01 3.202576e-02 3.284753e-01 -1.498395e-01
## [961] -1.290523e+00 -5.647034e-01 -1.670944e+00 -8.933681e-01 -6.794873e-03
## [966] 3.677879e-01 -6.329348e-01 1.592931e+00 -2.565216e+00 7.984405e-01
## [971] 9.820522e-01 4.341561e-01 1.714618e+00 1.100938e+00 1.471831e+00
## [976] 2.872211e-01 -9.200646e-02 -5.603371e-01 -1.205059e+00 -1.622053e+00
## [981] 2.629682e-01 -5.287396e-01 1.253856e+00 -1.455286e-01 -2.377610e-01
## [986] 1.124305e+00 9.514621e-01 -3.884070e-01 -5.958029e-01 1.229423e+00
## [991] -4.575000e-02 2.154987e+00 -5.293851e-01 4.443761e-01 -6.529698e-02
## [996] 1.374856e+00 -9.477124e-01 -4.901842e-01 -2.220450e-01 -6.790426e-01
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 12.858202 10.450053 7.973499 9.804130 11.705375 9.140396 8.684837
## [8] 6.904541 9.447328 8.506702 10.468682 9.199943 10.865112 8.755689
## [15] 10.620495 11.098420 10.449749 9.514995 8.612542 8.629453 10.132214
## [22] 10.870784 9.555070 9.599892 10.702501 11.633033 11.157602 10.842463
## [29] 11.881683 9.645046 8.255140 12.162382 12.073453 10.455491 8.541129
## [36] 10.710455 12.643852 8.355286 10.596005 9.051496 8.803906 8.627566
## [43] 9.443287 11.701000 11.904993 9.350054 8.706583 12.217386 11.122724
## [50] 11.138865 10.640437 9.248904 9.701436 10.134808 10.984945 8.873166
## [57] 12.415597 11.241851 10.041725 9.963063 9.955581 11.341549 11.343044
## [64] 8.751038 10.298108 8.909491 8.502412 10.147688 7.893566 12.413980
## [71] 9.268398 10.617186 9.871219 11.625209 10.200729 9.502388 9.696322
## [78] 10.189461 10.207098 9.978578 10.767468 9.340189 10.687299 10.420433
## [85] 12.020486 11.411679 12.245207 10.654938 11.533057 10.469342 10.117874
## [92] 11.785222 10.723962 9.424680 10.887535 9.786850 9.351778 11.688869
## [99] 11.028409 9.314565 13.401882 9.732396 10.128195 12.937801 8.268736
## [106] 7.930497 7.240712 9.634912 11.473259 11.161942 9.233876 6.591847
## [113] 10.028025 11.919780 6.038375 9.746041 12.427613 11.376710 7.914191
## [120] 10.354814 9.667361 9.880528 10.591862 9.221200 10.755720 7.393746
## [127] 9.889811 7.229342 8.307046 10.087323 9.043552 12.085320 11.020317
## [134] 9.328496 11.961566 9.583389 10.981460 8.556313 9.718339 7.814994
## [141] 9.127152 8.568466 11.204308 9.102156 9.876655 9.033366 9.245308
## [148] 9.116725 11.910207 11.351994 8.431926 9.902107 10.904677 11.722683
## [155] 10.755089 10.290501 10.100888 10.909645 9.264179 10.589112 12.269007
## [162] 9.380865 10.611334 9.548785 9.207968 5.897152 7.862949 10.916942
## [169] 11.077137 9.965263 10.603848 10.768878 9.426520 11.629979 11.615665
## [176] 9.730581 9.166736 9.279551 9.774718 9.230228 11.911969 8.164626
## [183] 9.699774 9.996645 12.289181 9.605297 9.494172 9.531329 12.198329
## [190] 7.957018 9.075295 8.684309 12.126759 8.768028 7.315457 9.958315
## [197] 12.176043 11.650941 9.546992 9.067951 11.234876 9.514655 9.835454
## [204] 7.743964 11.071646 11.188005 9.358992 9.164330 11.631874 10.613095
## [211] 9.700522 9.997647 10.689112 9.942406 9.075333 10.510947 11.972301
## [218] 7.948874 11.208151 8.876299 10.824394 11.560989 13.097054 13.486209
## [225] 10.857442 8.133475 13.852441 7.978906 12.385980 9.734912 8.800432
## [232] 12.085913 10.580900 9.469683 9.373662 8.909205 8.684608 10.580798
## [239] 9.423482 10.159570 11.323376 10.837768 8.867331 9.942828 9.688696
## [246] 11.631777 9.721952 11.999982 11.392989 10.942976 9.470928 10.677681
## [253] 12.306570 10.113021 9.115334 11.490635 9.891692 8.498328 9.844758
## [260] 8.947376 9.592685 10.581147 9.223111 9.681138 8.134376 9.820114
## [267] 11.826635 8.640563 11.404768 10.242990 11.879221 9.604775 10.772525
## [274] 9.238371 10.360400 6.284950 6.893152 10.553055 9.647761 7.520120
## [281] 9.556595 12.329999 11.490455 10.402318 11.422096 7.092446 9.080324
## [288] 8.631418 9.646492 11.408877 10.477738 10.809337 8.226104 10.079772
## [295] 12.949151 7.507392 10.983795 9.540336 9.009895 9.864485 7.417869
## [302] 10.740889 9.308572 9.773703 11.434582 9.816908 9.940477 11.413812
## [309] 9.467228 8.363340 7.962255 8.011566 9.034747 9.264340 13.279177
## [316] 12.825380 8.740425 12.146229 11.648921 10.730196 8.739335 10.943186
## [323] 10.064625 9.672414 7.978168 9.176113 9.425653 7.934142 8.688109
## [330] 8.135524 8.840435 12.636475 11.160951 10.688278 9.268857 10.583222
## [337] 10.338536 12.129916 11.928040 11.590747 10.054775 7.318926 10.937615
## [344] 11.189277 11.848614 9.352623 12.321753 9.056826 10.803272 9.027816
## [351] 8.980377 8.687783 10.549128 8.693660 10.368668 7.700297 9.739361
## [358] 7.400834 9.257748 10.252715 9.833660 9.454472 11.801366 9.072245
## [365] 9.182189 11.452374 10.727548 9.796922 9.875479 10.507273 11.134131
## [372] 10.213844 9.931941 9.061758 11.282289 10.672451 7.735260 8.696164
## [379] 10.191437 10.584235 7.284486 10.788928 10.542705 11.766552 9.542601
## [386] 10.417421 9.483797 10.233731 9.344611 8.663897 8.446844 12.073200
## [393] 10.668761 10.701693 10.044902 9.347365 11.279909 11.068526 10.584092
## [400] 8.741879 8.797404 10.965583 10.701024 8.293406 9.218065 11.026014
## [407] 10.706391 11.508981 9.910122 9.775579 11.103863 8.170613 9.714673
## [414] 11.969255 7.822242 10.540815 8.063420 9.844739 9.084322 10.559020
## [421] 11.167489 13.285966 11.598804 9.817684 10.951220 8.250175 9.407186
## [428] 10.785726 9.923777 11.744880 10.723216 7.481911 12.592406 10.359029
## [435] 10.838510 11.339755 11.510344 8.085862 8.659511 10.141665 9.989575
## [442] 11.326565 10.783156 9.451459 13.184271 9.883683 8.470291 10.017013
## [449] 9.487431 10.271854 9.655017 11.776970 11.161131 8.668228 11.414378
## [456] 10.580480 8.885560 9.769623 10.081463 8.134739 11.653189 10.376632
## [463] 8.806937 8.771698 8.082600 8.600086 8.369857 10.001842 10.726682
## [470] 8.033985 9.343435 10.487011 11.475001 11.525434 11.391175 12.172212
## [477] 10.274308 9.137781 6.078494 14.078051 8.975279 8.343538 9.354721
## [484] 10.283261 7.965245 8.810825 8.885044 8.747697 11.151141 9.178326
## [491] 11.860422 8.580027 8.357955 8.957275 9.531758 11.105655 9.931108
## [498] 8.171034 10.378738 7.434567 10.803444 11.095122 10.826056 10.197245
## [505] 7.657616 9.700526 10.388954 6.907859 13.026878 10.711151 9.636707
## [512] 9.487991 9.822952 10.498332 9.868076 7.719956 12.371045 9.933829
## [519] 9.012460 12.763864 10.016078 13.211016 10.226910 9.124894 7.935177
## [526] 8.896317 10.624753 12.239066 10.153096 9.283131 8.462300 9.105876
## [533] 12.331300 9.588258 9.788015 10.045627 8.397816 11.380330 10.029814
## [540] 10.938033 10.553997 9.870539 8.792605 13.252664 12.382035 8.796898
## [547] 10.849519 8.467375 10.714796 11.227371 9.595585 9.533914 11.062022
## [554] 9.386233 12.581207 10.810762 7.188136 12.953817 8.574244 8.300827
## [561] 8.707167 7.339783 13.244647 9.170302 10.730277 8.876456 10.717515
## [568] 12.239513 8.508106 9.856236 11.405286 8.488080 9.470342 8.797175
## [575] 7.204711 10.299493 11.030463 9.655800 10.860950 12.444790 11.253246
## [582] 8.277880 8.817180 8.947783 9.917328 11.414059 11.976479 9.666041
## [589] 10.984156 12.697825 10.673886 10.753395 10.912767 10.085458 11.148138
## [596] 11.148181 12.369016 10.028256 10.511946 10.882988 11.437323 9.761198
## [603] 7.654628 10.583660 10.418859 10.467120 10.632943 9.886885 10.611258
## [610] 8.989468 9.907008 8.852660 10.169823 9.477050 8.228856 7.067630
## [617] 9.169030 9.397053 12.296873 10.144626 8.990192 10.319144 7.193988
## [624] 9.704787 7.990164 9.524352 9.293393 9.927994 9.923158 6.987689
## [631] 9.954964 10.289173 9.894746 7.864069 9.984701 9.430769 9.930460
## [638] 8.625004 7.927901 9.025538 10.129145 8.372316 11.022510 9.267801
## [645] 10.101582 11.861589 10.029420 11.799093 10.688462 7.487452 10.401111
## [652] 9.620537 8.961081 11.953499 11.324345 9.974565 13.168429 10.253012
## [659] 11.711577 8.643224 11.238344 10.099403 8.778691 9.257141 7.975115
## [666] 7.323719 11.971810 10.385331 10.070031 9.588081 9.792634 8.923643
## [673] 10.115565 10.127809 11.553225 8.347676 10.236204 10.896992 8.973038
## [680] 11.730299 9.193285 12.875374 9.927278 9.702941 11.007120 8.182762
## [687] 8.237191 9.532508 9.629628 10.639203 11.282989 7.779327 9.841453
## [694] 10.908446 10.288152 8.532280 9.800567 12.049866 9.177682 9.283223
## [701] 10.133465 9.653212 10.508403 8.576544 7.290885 9.334689 8.856388
## [708] 9.479334 11.900570 9.370373 7.944047 14.194136 9.152558 6.590012
## [715] 10.326404 9.680541 8.622651 10.111123 11.204618 10.531965 13.423958
## [722] 8.476359 10.515956 10.867089 11.664650 11.773994 11.636733 10.296259
## [729] 12.056620 9.231008 9.506599 11.299672 12.943716 8.507641 11.062469
## [736] 10.823858 10.385817 11.505285 11.617394 9.919874 10.846171 8.027122
## [743] 10.084414 11.129140 12.080421 10.303104 10.250848 10.437238 11.592183
## [750] 11.911966 10.382590 9.716509 9.720805 9.933256 9.489256 9.080723
## [757] 11.553500 9.469332 12.117977 8.733452 7.388666 9.751798 8.406310
## [764] 8.979235 10.400131 10.834120 11.949658 9.778500 9.599600 9.512204
## [771] 8.760611 10.005995 9.205645 9.552940 9.163635 9.368585 10.579151
## [778] 9.860382 10.968473 10.543131 10.598860 9.476668 11.552915 9.346468
## [785] 10.487576 9.221862 11.379269 8.372821 10.561787 9.903619 10.707832
## [792] 11.350889 11.090246 10.786376 11.048636 11.497259 11.288286 9.061557
## [799] 8.967561 11.364306 9.827197 7.414492 10.308290 10.481870 10.259202
## [806] 11.084093 7.017503 10.914135 9.614732 10.941418 8.454141 9.693754
## [813] 9.462195 11.376554 7.268569 8.921821 7.574261 9.517249 11.235884
## [820] 10.445435 11.014326 11.106054 10.457813 9.128757 10.775415 8.663882
## [827] 11.673654 11.163195 12.256713 10.724789 9.851802 9.846061 9.992491
## [834] 9.900028 10.682211 8.458173 9.528381 10.185105 7.657042 9.930346
## [841] 9.190073 9.718608 8.410603 8.949800 9.292042 10.520880 9.609677
## [848] 7.893512 10.119866 10.966762 11.201329 10.119363 9.197879 12.474925
## [855] 10.511912 9.676029 11.209840 8.741813 13.047551 11.356995 7.943111
## [862] 9.036008 11.231525 9.605832 10.510941 8.752998 10.626784 8.902926
## [869] 12.681329 9.953176 9.524386 10.411043 7.899455 11.060155 9.607261
## [876] 9.536025 10.016053 8.976997 10.373543 9.583726 9.644134 10.399501
## [883] 11.069852 9.206039 12.332846 8.641618 13.322059 11.616505 9.826443
## [890] 11.236897 9.611061 8.094135 8.968859 9.178086 13.090560 9.216666
## [897] 12.339203 9.556505 10.796612 7.971692 9.448064 8.752859 11.118736
## [904] 8.983777 7.820510 10.432668 11.359030 12.342615 9.636642 11.729757
## [911] 9.757715 13.476826 8.600476 9.636584 12.889550 10.249955 8.849509
## [918] 5.108820 9.906377 9.844527 9.230932 10.200697 8.321209 12.261194
## [925] 6.731665 8.063948 7.795110 9.870286 9.795484 10.974811 10.298370
## [932] 10.106334 8.840706 11.066609 10.312099 11.127701 11.904224 10.475510
## [939] 8.434056 8.905210 10.894513 8.218543 9.278008 9.897465 9.472427
## [946] 10.085902 12.924584 11.699472 10.817617 12.152696 9.564680 6.640854
## [953] 8.028104 10.104044 10.176515 12.140814 8.195849 7.853641 11.098955
## [960] 11.306532 8.454365 10.135233 9.045784 9.318619 10.854664 10.772542
## [967] 8.848331 12.477270 7.726578 12.485904 12.171552 10.929178 12.251802
## [974] 14.240875 10.419803 8.928807 10.431183 9.405601 10.179110 8.454998
## [981] 11.408781 9.985489 11.914763 9.471801 9.762661 9.512483 11.745518
## [988] 9.224966 8.135371 11.547913 11.293135 13.889097 7.345768 12.636716
## [995] 9.296362 10.988637 10.474198 10.120656 11.016877 7.346064
# create groups for different values of X
(group <- rep(1,1000)) # a vector consisting of 1000 elements
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [778] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [815] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [852] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [889] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [926] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [963] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1000] 1
group[xAxis > -1.5] <- 2
group[xAxis > -.5] <- 3
group[xAxis > .5] <- 4
group[xAxis > 1.5] <- 5
group
## [1] 5 3 3 2 4 1 3 1 3 1 2 4 3 4 4 3 3 2 3 2 3 3 3 3 3 5 3 5 4 4 3 4 5 2 1 3 5
## [38] 2 3 2 3 3 4 5 4 4 4 4 3 3 3 2 2 2 3 3 2 4 2 3 3 3 3 2 3 2 2 3 3 4 3 5 3 3
## [75] 4 1 3 3 3 4 4 2 4 4 4 4 4 3 3 2 3 5 3 2 2 3 2 3 3 3 5 4 3 5 2 3 2 4 4 4 3
## [112] 1 3 4 2 3 5 4 1 2 3 3 3 3 3 2 3 2 2 2 3 3 4 3 3 3 4 2 3 1 3 3 4 2 2 2 3 2
## [149] 3 4 2 3 4 3 4 1 3 4 3 3 5 3 4 3 4 1 2 4 4 3 4 4 2 5 4 2 2 2 4 2 3 1 3 3 4
## [186] 4 3 4 5 2 2 3 3 2 2 5 5 3 1 3 3 3 4 2 4 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 5
## [223] 3 4 2 2 4 2 4 3 2 4 3 2 3 2 1 4 4 4 4 4 2 5 4 4 2 4 4 4 2 4 5 4 3 4 3 2 4
## [260] 4 3 4 3 2 2 1 5 2 4 3 3 2 3 2 3 2 2 3 2 1 3 4 4 3 3 1 2 3 3 3 3 4 2 2 4 1
## [297] 4 3 1 2 2 3 2 4 3 4 4 2 2 2 2 2 2 3 5 4 1 4 4 4 2 4 4 2 2 2 3 2 3 3 2 4 3
## [334] 4 3 4 4 5 3 3 4 1 4 4 3 2 4 2 3 2 2 1 4 2 4 2 3 2 3 3 2 3 3 2 4 4 4 2 3 3
## [371] 3 3 3 2 3 3 2 3 3 2 1 4 2 5 3 3 2 3 4 2 3 4 3 3 2 4 4 3 4 2 1 3 3 2 3 3 3
## [408] 4 3 2 4 4 3 4 2 3 3 3 3 4 4 5 4 2 2 1 3 3 2 4 3 1 5 3 2 3 4 2 1 3 3 3 4 2
## [445] 4 2 3 3 2 4 3 4 4 2 3 4 3 4 4 2 3 3 3 2 1 2 1 4 2 3 2 3 4 3 3 4 2 3 2 4 3
## [482] 3 3 3 2 3 2 3 3 4 4 3 3 3 3 4 3 4 3 1 3 3 4 3 1 3 3 2 3 4 4 2 4 3 4 1 5 4
## [519] 2 3 3 4 3 4 1 1 4 4 3 1 2 3 5 3 4 4 2 3 4 3 2 4 2 5 4 2 4 3 4 3 2 5 3 4 4
## [556] 2 1 5 2 2 1 2 4 2 5 3 3 3 3 3 4 3 2 2 1 3 4 2 2 4 3 1 2 3 4 4 3 2 3 5 3 2
## [593] 2 3 3 4 5 3 3 3 3 3 1 3 3 1 4 3 4 3 2 2 4 3 2 1 3 2 3 3 2 2 2 3 1 2 2 3 3
## [630] 2 5 3 2 2 4 1 3 1 3 3 4 3 3 4 2 4 2 4 3 2 3 2 2 4 3 4 5 4 4 3 4 3 2 3 2 2
## [667] 5 4 3 3 3 3 2 4 4 1 3 3 2 3 4 4 4 3 3 2 2 2 3 3 4 3 3 4 4 3 2 3 3 3 4 3 4
## [704] 3 1 3 2 3 4 2 1 5 2 1 4 3 1 2 4 4 4 1 2 4 5 4 3 1 5 2 3 3 5 1 2 4 3 3 3 3
## [741] 3 2 4 3 4 4 3 4 3 4 4 3 3 2 4 2 5 3 5 2 3 2 2 3 3 3 3 4 2 4 3 3 2 2 3 2 3
## [778] 3 4 3 3 2 4 2 4 3 4 2 4 3 3 4 5 3 3 3 5 2 3 3 3 2 2 4 3 3 1 3 3 3 2 3 2 4
## [815] 1 2 1 3 5 3 3 4 3 3 4 3 4 2 5 3 4 3 3 2 4 3 2 3 3 4 2 3 2 2 3 4 2 2 2 4 4
## [852] 3 3 4 2 2 5 2 5 4 2 5 4 3 3 2 3 2 5 4 3 3 1 3 2 3 1 2 3 2 3 2 4 2 4 3 4 4
## [889] 2 4 4 2 2 3 5 3 4 3 3 3 5 2 3 3 2 4 4 4 2 4 3 4 3 3 3 3 3 1 4 3 2 4 1 4 1
## [926] 2 2 2 4 3 4 3 2 3 5 3 4 3 3 2 3 2 3 3 2 2 5 5 4 3 3 1 1 5 3 4 3 3 3 3 2 2
## [963] 1 2 3 3 2 5 1 4 4 3 5 4 4 3 3 2 2 1 3 2 4 3 3 4 4 3 2 4 3 5 2 3 3 4 2 3 3
## [1000] 2
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 1.587920e+00 12.858202 5
## 2 -1.722684e-01 10.450053 3
## 3 1.739227e-01 7.973499 3
## 4 -9.073087e-01 9.804130 2
## 5 1.092217e+00 11.705375 4
## 6 -2.492513e+00 9.140396 1
## 7 -2.658749e-01 8.684837 3
## 8 -1.983148e+00 6.904541 1
## 9 -1.569309e-01 9.447328 3
## 10 -1.529130e+00 8.506702 1
## 11 -7.915475e-01 10.468682 2
## 12 7.357725e-01 9.199943 4
## 13 2.035541e-01 10.865112 3
## 14 6.193544e-01 8.755689 4
## 15 5.212013e-01 10.620495 4
## 16 -4.698678e-01 11.098420 3
## 17 -4.777037e-01 10.449749 3
## 18 -1.006837e+00 9.514995 2
## 19 -1.144046e-01 8.612542 3
## 20 -5.595166e-01 8.629453 2
## 21 -1.326968e-01 10.132214 3
## 22 -1.469577e-01 10.870784 3
## 23 1.623082e-01 9.555070 3
## 24 -4.025620e-01 9.599892 3
## 25 -4.825773e-01 10.702501 3
## 26 1.575530e+00 11.633033 5
## 27 2.314619e-02 11.157602 3
## 28 1.841717e+00 10.842463 5
## 29 1.141920e+00 11.881683 4
## 30 7.576980e-01 9.645046 4
## 31 -1.121785e-01 8.255140 3
## 32 5.612200e-01 12.162382 4
## 33 2.396383e+00 12.073453 5
## 34 -5.824677e-01 10.455491 2
## 35 -2.166031e+00 8.541129 1
## 36 -4.547742e-01 10.710455 3
## 37 2.624247e+00 12.643852 5
## 38 -8.974293e-01 8.355286 2
## 39 1.197871e-01 10.596005 3
## 40 -5.985503e-01 9.051496 2
## 41 4.043213e-01 8.803906 3
## 42 3.786685e-01 8.627566 3
## 43 6.295023e-01 9.443287 4
## 44 2.018307e+00 11.701000 5
## 45 6.157220e-01 11.904993 4
## 46 9.800435e-01 9.350054 4
## 47 5.662733e-01 8.706583 4
## 48 1.449137e+00 12.217386 4
## 49 2.179945e-01 11.122724 3
## 50 2.424981e-01 11.138865 3
## 51 4.917759e-02 10.640437 3
## 52 -6.647182e-01 9.248904 2
## 53 -7.355422e-01 9.701436 2
## 54 -5.115664e-01 10.134808 2
## 55 3.099128e-01 10.984945 3
## 56 2.965093e-01 8.873166 3
## 57 -1.006876e+00 12.415597 2
## 58 7.842893e-01 11.241851 4
## 59 -6.445985e-01 10.041725 2
## 60 -1.095109e-01 9.963063 3
## 61 -3.450017e-01 9.955581 3
## 62 4.524661e-01 11.341549 3
## 63 -2.148408e-01 11.343044 3
## 64 -1.262717e+00 8.751038 2
## 65 -4.017046e-01 10.298108 3
## 66 -1.322333e+00 8.909491 2
## 67 -8.591730e-01 8.502412 2
## 68 -3.602382e-01 10.147688 3
## 69 7.131889e-02 7.893566 3
## 70 1.157999e+00 12.413980 4
## 71 -4.894134e-01 9.268398 3
## 72 1.736630e+00 10.617186 5
## 73 -1.424746e-02 9.871219 3
## 74 -1.053363e-01 11.625209 3
## 75 6.175860e-01 10.200729 4
## 76 -1.609722e+00 9.502388 1
## 77 2.405156e-01 9.696322 3
## 78 -4.451738e-01 10.189461 3
## 79 -4.250929e-01 10.207098 3
## 80 6.646878e-01 9.978578 4
## 81 1.238249e+00 10.767468 4
## 82 -7.737099e-01 9.340189 2
## 83 7.781273e-01 10.687299 4
## 84 7.892664e-01 10.420433 4
## 85 1.209510e+00 12.020486 4
## 86 9.856757e-01 11.411679 4
## 87 1.218858e+00 12.245207 4
## 88 4.715056e-01 10.654938 3
## 89 4.109596e-01 11.533057 3
## 90 -8.660229e-01 10.469342 2
## 91 -4.768793e-01 10.117874 3
## 92 1.579949e+00 11.785222 5
## 93 4.744178e-01 10.723962 3
## 94 -8.050216e-01 9.424680 2
## 95 -8.215773e-01 10.887535 2
## 96 -1.324710e-01 9.786850 3
## 97 -1.124988e+00 9.351778 2
## 98 1.892529e-01 11.688869 3
## 99 3.517427e-01 11.028409 3
## 100 -1.151522e-01 9.314565 3
## 101 2.393821e+00 13.401882 5
## 102 7.954308e-01 9.732396 4
## 103 -1.442011e-01 10.128195 3
## 104 2.093998e+00 12.937801 5
## 105 -1.237386e+00 8.268736 2
## 106 8.019452e-02 7.930497 3
## 107 -8.551382e-01 7.240712 2
## 108 1.154670e+00 9.634912 4
## 109 6.630603e-01 11.473259 4
## 110 1.020724e+00 11.161942 4
## 111 8.666457e-04 9.233876 3
## 112 -1.551402e+00 6.591847 1
## 113 -3.246046e-01 10.028025 3
## 114 9.575904e-01 11.919780 4
## 115 -5.042361e-01 6.038375 2
## 116 -3.381010e-01 9.746041 3
## 117 1.738801e+00 12.427613 5
## 118 7.979930e-01 11.376710 4
## 119 -1.833400e+00 7.914191 1
## 120 -5.311788e-01 10.354814 2
## 121 -3.850130e-01 9.667361 3
## 122 8.476324e-02 9.880528 3
## 123 4.959785e-01 10.591862 3
## 124 -9.061115e-02 9.221200 3
## 125 9.362350e-02 10.755720 3
## 126 -1.163593e+00 7.393746 2
## 127 -2.406275e-02 9.889811 3
## 128 -1.387455e+00 7.229342 2
## 129 -1.004692e+00 8.307046 2
## 130 -1.032002e+00 10.087323 2
## 131 -2.069973e-01 9.043552 3
## 132 3.614803e-01 12.085320 3
## 133 6.291514e-01 11.020317 4
## 134 2.012165e-01 9.328496 3
## 135 4.968673e-01 11.961566 3
## 136 -1.183342e-01 9.583389 3
## 137 5.005629e-01 10.981460 4
## 138 -1.205854e+00 8.556313 2
## 139 -1.421657e-01 9.718339 3
## 140 -2.034007e+00 7.814994 1
## 141 4.231552e-01 9.127152 3
## 142 2.294028e-01 8.568466 3
## 143 8.328690e-01 11.204308 4
## 144 -5.386442e-01 9.102156 2
## 145 -7.319092e-01 9.876655 2
## 146 -6.466631e-01 9.033366 2
## 147 -1.976410e-01 9.245308 3
## 148 -8.517918e-01 9.116725 2
## 149 5.554984e-02 11.910207 3
## 150 6.043288e-01 11.351994 4
## 151 -8.353317e-01 8.431926 2
## 152 4.876827e-01 9.902107 3
## 153 1.309247e+00 10.904677 4
## 154 8.990664e-02 11.722683 3
## 155 7.229827e-01 10.755089 4
## 156 -2.076048e+00 10.290501 1
## 157 6.287588e-02 10.100888 3
## 158 5.656863e-01 10.909645 4
## 159 -9.701690e-02 9.264179 3
## 160 3.679533e-01 10.589112 3
## 161 1.770489e+00 12.269007 5
## 162 1.897416e-01 9.380865 3
## 163 1.346829e+00 10.611334 4
## 164 -4.708850e-01 9.548785 3
## 165 7.339856e-01 9.207968 4
## 166 -2.737404e+00 5.897152 1
## 167 -1.439972e+00 7.862949 2
## 168 1.363184e+00 10.916942 4
## 169 1.314152e+00 11.077137 4
## 170 3.917118e-01 9.965263 3
## 171 8.432119e-01 10.603848 4
## 172 6.795898e-01 10.768878 4
## 173 -1.388424e+00 9.426520 2
## 174 1.933951e+00 11.629979 5
## 175 6.189742e-01 11.615665 4
## 176 -1.063640e+00 9.730581 2
## 177 -8.480410e-01 9.166736 2
## 178 -7.392480e-01 9.279551 2
## 179 6.982161e-01 9.774718 4
## 180 -9.030033e-01 9.230228 2
## 181 -6.119994e-02 11.911969 3
## 182 -1.567804e+00 8.164626 1
## 183 4.692320e-01 9.699774 3
## 184 3.727023e-01 9.996645 3
## 185 1.153730e+00 12.289181 4
## 186 1.351707e+00 9.605297 4
## 187 1.825505e-01 9.494172 3
## 188 5.648123e-01 9.531329 4
## 189 2.252145e+00 12.198329 5
## 190 -1.318443e+00 7.957018 2
## 191 -1.484874e+00 9.075295 2
## 192 -3.132031e-01 8.684309 3
## 193 3.917951e-01 12.126759 3
## 194 -5.223503e-01 8.768028 2
## 195 -1.308493e+00 7.315457 2
## 196 1.923254e+00 9.958315 5
## 197 2.082199e+00 12.176043 5
## 198 2.500971e-01 11.650941 3
## 199 -1.603403e+00 9.546992 1
## 200 -4.203267e-01 9.067951 3
## 201 7.512202e-02 11.234876 3
## 202 1.459077e-01 9.514655 3
## 203 5.361882e-01 9.835454 4
## 204 -7.550430e-01 7.743964 2
## 205 1.231330e+00 11.071646 4
## 206 2.283683e-01 11.188005 3
## 207 2.635353e-01 9.358992 3
## 208 -6.494000e-01 9.164330 2
## 209 -1.708576e-01 11.631874 3
## 210 1.550781e-01 10.613095 3
## 211 -8.159940e-01 9.700522 2
## 212 -1.888456e-01 9.997647 3
## 213 3.672309e-01 10.689112 3
## 214 3.036284e-01 9.942406 3
## 215 -1.189241e-01 9.075333 3
## 216 4.626075e-01 10.510947 3
## 217 -2.751402e-03 11.972301 3
## 218 2.713570e-01 7.948874 3
## 219 -2.605269e-01 11.208151 3
## 220 -2.221418e-01 8.876299 3
## 221 4.457677e-01 10.824394 3
## 222 1.696281e+00 11.560989 5
## 223 3.752946e-01 13.097054 3
## 224 7.353636e-01 13.486209 4
## 225 -1.237369e+00 10.857442 2
## 226 -1.253690e+00 8.133475 2
## 227 5.386757e-01 13.852441 4
## 228 -1.311890e+00 7.978906 2
## 229 1.456827e+00 12.385980 4
## 230 -4.190606e-01 9.734912 3
## 231 -1.014613e+00 8.800432 2
## 232 6.227033e-01 12.085913 4
## 233 -3.857563e-01 10.580900 3
## 234 -5.775524e-01 9.469683 2
## 235 -4.829732e-01 9.373662 3
## 236 -1.424377e+00 8.909205 2
## 237 -1.673106e+00 8.684608 1
## 238 8.894994e-01 10.580798 4
## 239 1.393524e+00 9.423482 4
## 240 6.901717e-01 10.159570 4
## 241 8.683768e-01 11.323376 4
## 242 1.041279e+00 10.837768 4
## 243 -5.090490e-01 8.867331 2
## 244 1.502113e+00 9.942828 5
## 245 1.020895e+00 9.688696 4
## 246 8.157094e-01 11.631777 4
## 247 -6.540571e-01 9.721952 2
## 248 8.572644e-01 11.999982 4
## 249 8.378394e-01 11.392989 4
## 250 7.798819e-01 10.942976 4
## 251 -7.527332e-01 9.470928 2
## 252 1.118876e+00 10.677681 4
## 253 2.166046e+00 12.306570 5
## 254 5.810223e-01 10.113021 4
## 255 3.827395e-01 9.115334 3
## 256 8.871889e-01 11.490635 4
## 257 -4.156320e-01 9.891692 3
## 258 -1.203535e+00 8.498328 2
## 259 6.086916e-01 9.844758 4
## 260 6.494979e-01 8.947376 4
## 261 -2.540508e-01 9.592685 3
## 262 7.188356e-01 10.581147 4
## 263 4.650806e-01 9.223111 3
## 264 -9.542081e-01 9.681138 2
## 265 -1.202039e+00 8.134376 2
## 266 -1.955842e+00 9.820114 1
## 267 1.748330e+00 11.826635 5
## 268 -9.600780e-01 8.640563 2
## 269 1.309636e+00 11.404768 4
## 270 2.063876e-01 10.242990 3
## 271 4.872189e-01 11.879221 3
## 272 -8.300234e-01 9.604775 2
## 273 -4.302574e-01 10.772525 3
## 274 -8.390864e-01 9.238371 2
## 275 -2.043572e-01 10.360400 3
## 276 -7.685188e-01 6.284950 2
## 277 -9.179052e-01 6.893152 2
## 278 -4.296205e-01 10.553055 3
## 279 -9.687062e-01 9.647761 2
## 280 -2.101671e+00 7.520120 1
## 281 -1.113589e-01 9.556595 3
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## 952 -1.666823e+00 6.640854 1
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## 999 -2.220450e-01 11.016877 3
## 1000 -6.790426e-01 7.346064 2
# creates plot object using ggplot
plot <- ggplot(sample_data, aes(x = xAxis, y= yAxis, col = as.factor(group)))+
geom_point()+theme(legend.position = "none")
# Display plot
plot
# Insert marginal ditribution using marginal function
ggMarginal(plot,type= 'histogram',groupColour=TRUE,groupFill=TRUE)
